'''
Author: SlytherinGe
LastEditTime: 2021-04-08 16:14:16
'''
_base_ = [
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    type='RetinaNet',
    pretrained='/media/gejunyao/Disk/Gejunyao/develop/pretrained_model/resnet50-twoway.pth',
    backbone=dict(
        type='TwoWay_ResNet',
        depth=50,
        in_channels = 3,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5),
    bbox_head=dict(
        type='RetinaHead',
        num_classes=9,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0,
            ignore_iof_thr=-1),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))


# dataset settings
dataset_type = 'VEDAIDatasetMutiChannel'
data_root = '/media/gejunyao/Disk1/Customized Datasets/VEDAI_VOC/'
img_norm_cfg = dict(
    mean=[122.81532489, 123.34766762, 109.80794601, 170.35183843, 170.35183843, 170.35183843],
    std=[37.03608904, 33.56975757, 34.81962266, 27.46958872, 27.46958872, 27.46958872], to_rgb=False)
train_pipeline = [
    dict(type='LoadMultiChannelImageFromFiles', color_type='color'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1024, 1024), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadMultiChannelImageFromFiles', color_type='color'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1024, 1024),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=16,
    train=dict(
        _delete_=True,
        type='RepeatDataset',
        times=4,
        # type='ClassBalancedDataset', 
        # oversample_thr=1e-2,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root + 'ImageSets/imageset1024/trainval.txt',
            img_prefix=data_root,
            pipeline=train_pipeline)),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'ImageSets/imageset1024/test.txt',
        img_prefix=data_root,
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'ImageSets/imageset1024/test.txt',
        img_prefix=data_root,
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='mAP')
# optimizer
optimizer = dict(type='SGD', lr=0.0002, momentum=0.8, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
runner = dict(type='EpochBasedRunner', max_epochs=48)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=50,
    warmup_ratio=0.001,
    step=[36, 40, 46])
log_config = dict(
    interval=10,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])